At present, typical keyword searches entered into a search engine cause the performance of a web crawl, wherein the best matches to the keywords searched that are located by the web crawl are returned as the search results. However, the matches returned via this methodology often poorly matches what the user intended to locate via the user's search. The poor matches are frequently reflective of the fact that different search terms can mean different things to different people—that is, people having different demographic or personal characteristics may have similar views of what would constitute relevant results to a user search. Available search engines fail to take advantage of this similarity of certain users in order to enable improvement in search results.
Additionally, although the prior art may make available the re-use of a prior search by a searching user, such re-use of a search is not likely useful, unless the prior searcher had a greater expertise in the area searched than did the present searcher. Thus, existing search engines fail to recognize and make use of the expertise of others in endeavoring to return the best search results for a searching user.
Therefore, the need exists for a search engine, system and method that returns search results based on those results deemed most useful by experts in the field searched, and that allows for results to be filtered based on those experts most similar to the searching user in demographic and personal information.